Rasmus Kyng: Catalogue data in Spring Semester 2021

Name Prof. Dr. Rasmus Kyng
FieldTheoretical Computer Science
Professur Theoretische Informatik
ETH Zürich, CAB H 33.1
Universitätstrasse 6
8092 Zürich
DepartmentComputer Science
RelationshipAssistant Professor (Tenure Track)

252-4225-00LPresenting Theoretical Computer Science Restricted registration - show details
Number of participants limited to 24.

The deadline for deregistering expires at the end of the second week of the semester. Students who are still registered after that date, but do not attend the seminar, will officially fail the seminar.
2 credits2SB. Gärtner, M. Ghaffari, R. Kyng, D. Steurer, E. Welzl
AbstractStudents present current or classical results from theoretical computer science.
ObjectiveStudents learn to read, understand and present results from theoretical computer science. The main focus and deliverable is a good presentation of 45 minutes that can easily be followed and understood by the audience.
ContentStudents present current or classical results from theoretical computer science.
Prerequisites / NoticeThe seminar takes place as a block seminar on two Saturdays in April and/or May. Each presentation is jointly prepared and given by two students (procedure according to the seminar's Moodle page).
All students must attend all presentations. Participation requires successful completion of the first year, or instructor approval.
263-4400-00LAdvanced Graph Algorithms and Optimization Information 8 credits3V + 1U + 3AR. Kyng, M. Probst
AbstractThis course will cover a number of advanced topics in optimization and graph algorithms.
ObjectiveThe course will take students on a deep dive into modern approaches to
graph algorithms using convex optimization techniques.

By studying convex optimization through the lens of graph algorithms,
students should develop a deeper understanding of fundamental
phenomena in optimization.

The course will cover some traditional discrete approaches to various graph
problems, especially flow problems, and then contrast these approaches
with modern, asymptotically faster methods based on combining convex
optimization with spectral and combinatorial graph theory.
ContentStudents should leave the course understanding key
concepts in optimization such as first and second-order optimization,
convex duality, multiplicative weights and dual-based methods,
acceleration, preconditioning, and non-Euclidean optimization.

Students will also be familiarized with central techniques in the
development of graph algorithms in the past 15 years, including graph
decomposition techniques, sparsification, oblivious routing, and
spectral and combinatorial preconditioning.
Prerequisites / NoticeThis course is targeted toward masters and doctoral students with an
interest in theoretical computer science.

Students should be comfortable with design and analysis of algorithms, probability, and linear algebra.

Having passed the course Algorithms, Probability, and Computing (APC) is highly recommended, but not formally required. If you are not
sure whether you're ready for this class or not, please consult the